The subject matter described herein generally relates to the monitoring of the Quality-of-Experience (QoE) of a mobile user of a wireless network without introducing any additional packets or requiring user feedback.
Wireless networks are expanding their offerings of live or near-live multimedia services, including mobile video calls, voice and/or video conferencing, video streaming, mobile informational devices (for example, mobile telemedicine video carts), and service appliances. In addition, the availability of fourth generation wireless networks (4G), which enable high bandwidth and latency sensitive applications over wireless, is increasing. Quality-of-Service (QoS) parameters, such as packet delay, loss, and jitter, provided by the underlying network to the service flow varies with time. In addition, the QoE is sensitive to even small changes in the QoS parameter values. As such, in order to provide satisfactory multimedia service, it is important to continuously monitor the QoE perceived by the mobile user.
Existing QoE metrics for multimedia services can be categorized in multiple ways. For example, QoE metrics for multimedia services can be categorized according to service type, such as whether the application is utilizing voice (mean opinion scores [MoS]) or video (peak signal-to-noise ratio PSNR). QoE metrics can be categorized subjectively through evaluation by actual users of a particular service (MoS) or through objective quality metrics, such as analytical models like PSNR. QoE metrics for can be categorized based on how much reference information is required about the original (reference) multi-media flow (for example, full-reference, reduced-reference and no-reference). QoE metrics for can be categorized based on whether spatial or temporal distortions (initial buffering time, stalls) are measured, whether user feedback is required, and/or whether probes (QoE robots) or new messages are utilized. QoE metrics for can be categorized based on location of the QoE module (at the source, end-user or in the network), and whether live QoE monitoring or offline QoE computation is employed.
The monitoring and management of Quality-of-Experience (QoE) is important to high-level wireless communication networks, such as the fourth generation wireless network (4G). This is due to such networks volume intensive rich media services, scarce wireless resources, the pack value per wireless bit, and prioritizing among sessions to increase value per wireless bit. Wireless communication networks, such as 4G networks, are higher bandwidth compared to 3G networks, due primarily to the ability to provide, among other services, rich media services, data downloads, and multiple sessions simultaneously. As such, bottlenecks will occur between network base stations and end users devices. Thus, there is a need for specific intervention to manage such services, as the challenge to the wireless link becomes the bottleneck created by supporting all of a network's mobile users.
In existing systems, wireless links have a fixed number of transmission slots. In addition, wireless conditions can vary depending on, among other conditions, geographic area, number of users, and data load. Furthermore, services delivered on wireless links, such as media services, may not be provided at a constant rate and network base stations may not have the same data and connection capabilities. Thus, a need exists for an intelligent mechanism to manage and alleviate any such network bottlenecks.
Existing solutions for remote monitoring of QoE of a mobile user either modify existing messages or introduce new packets or messages, while other existing solutions are application specific. In addition, such solutions are not easy to deploy and are not scalable when continuous monitoring of QoE of a large number of mobile users is required. Furthermore, certain existing solutions are based on non-standard protocols and, as such, are unable to support diverse end-user equipment and applications.